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‘The algorithm is hacked’: analysis of technology delusions in a modern-day cohort

Published online by Cambridge University Press:  03 November 2025

Alaina V. Burns*
Affiliation:
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
Kyle Nelson
Affiliation:
UCLA Veterans Initiatives and Partnerships, Los Angeles, CA, USA
Haley Wang
Affiliation:
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
Erin M. Hegarty
Affiliation:
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
Alexander B. Cohn
Affiliation:
Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
*
Correspondence: Alaina V. Burns. Email: avburns@mednet.ucla.edu
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Abstract

Background

Research exploring delusions among individuals with psychosis often focuses on form, rather than content, and on prevalence, rather than change in a cohort over time. While delusional forms are mostly consistent across cultures and historical periods, the content of delusions is shaped by sociopolitical factors.

Aims

We explored the form and content of delusions in a modern sample of individuals with psychosis, examining the extent to which the internet and new technologies become incorporated into delusional frameworks. We investigated whether there was a change in the prevalence of technology delusions over time and how gender, age and education level impacted the probability that a subject would experience technology delusions.

Method

We reviewed the medical records of 228 adults with psychosis who were seeking treatment at a large academic medical centre between 2016 and 2024 and extracted any description of delusional thought content. We characterised delusions into subtypes and explored the ways these delusions feature the internet and new technologies. To examine temporal trends in the content of delusions, we conducted a binary logistic regression analysis with year as the predictor variable and the presence of technology-related content in delusions as the outcome variable.

Results

Most subjects (88.2%) reported delusional thought content, with over half (51.7%) describing technology delusions. Logistic regression between the year and technology-related delusion outcome revealed statistically significant (β = 0.139, p = 0.038, 95% CI (0.008, 0.270)) correlation. For each 1-year increase, the odds of a subject presenting with technology delusions increased by approximately 15% (odds ratio 1.15).

Conclusions

Among individuals with psychotic disorders, the internet and new technologies are increasingly salient in delusional frameworks. Clinicians should be aware of these themes while eliciting symptoms from patients and also while educating trainees.

Information

Type
Original Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal College of Psychiatrists
Figure 0

Table 1 Demographic characteristics of the study sample

Figure 1

Fig. 1 Predicted probability of technology-related delusions by year of admission. The solid line represents the predicted probability of technology-related delusions (1 = present, 0 = absent) based on the logistic regression model with admission year as the predictor. The shaded region indicates the 95% confidence interval for these predictions. Dots represent the actual observations in the data-set, with slight vertical jittering applied to improve visibility of overlapping points. The model demonstrates a significant positive relationship between later admission years and increased probability of technology-related delusions (β = 0.139, p = 0.038, 95% CI (0.008, 0.270)), with each additional year associated with 15% higher odds of presenting with technology-related delusions.

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